That depends on the size of the population being sampled from, the margin of error, and the confidence level.
For a huge effect like the one shown in the study, where one side performed 2x as well as the other, a sample size of 48 is more than large enough to say that the result is statistically significant. If there was as small effect, that wouldn't be the case.
Put it this way. You want to find whether people from California prefer Taco Bell or Pizza Hut, so you randomly sample 100 people. If all 100 people say Taco Bell, then you can be reasonably confident that more people from California prefer Taco Bell. Because if at least 51% of your population preferred Pizza Hut, the odds of not getting one of those people in your sample are minuscule (the odds of getting all Taco Bell people in your sample if 49% of the population prefers Taco Bell is 0.49^100).
If 51 prefer Taco Bell and 49 prefer Pizza Hut, your confidence level is too low to be useful--you need a larger sample size.
That depends on the size of the population being sampled from, the margin of error, and the confidence level.
For a huge effect like the one shown in the study, where one side performed 2x as well as the other, a sample size of 48 is more than large enough to say that the result is statistically significant. If there was as small effect, that wouldn't be the case.
Put it this way. You want to find whether people from California prefer Taco Bell or Pizza Hut, so you randomly sample 100 people. If all 100 people say Taco Bell, then you can be reasonably confident that more people from California prefer Taco Bell. Because if at least 51% of your population preferred Pizza Hut, the odds of not getting one of those people in your sample are minuscule (the odds of getting all Taco Bell people in your sample if 49% of the population prefers Taco Bell is 0.49^100).
If 51 prefer Taco Bell and 49 prefer Pizza Hut, your confidence level is too low to be useful--you need a larger sample size.